effects of tax incentives on financial performance of
TRANSCRIPT
EFFECTS OF TAX INCENTIVES ON FINANCIAL PERFORMANCE OF
SAVINGS AND CREDIT COOPERATIVE SOCIETIES IN NAIROBI
COUNTY
BY
BONIFACE KINYUA
D63/5939/2017
A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENT FOR THE AWARD OF THE DEGREE OF
MASTER OF SCIENCE IN FINANCE, SCHOOL OF BUSINESS,
UNIVERSITY OF NAIROBI.
NOVEMBER, 2019
ii
DECLARATION
This research project has never been presented in any other University or institution for academic
purposes.
Signed______________________________ Date _______________________________
BONIFACE KINYUA,
D63/5939/2017.
This research project has been submitted for examination with my approval as the University
Supervisor.
Signed______________________________ Date _______________________________
Miss. Hellen Kinyua.
Signed______________________________ Date _______________________________
Dr. Kennedy Okiro.
Lecturers, Department of Finance and Accounting,
School of Business, University of Nairobi.
iii
ACKNOWLEDGEMENTS
First and foremost, I take this opportunity to thank God for granting me good health and
time to study. I appreciate my supervisors Miss Hellen Kinyua and Dr. Kennedy Okiro
for their tremendous support in carrying out this research. Deep appreciation to my wife
and kids for their encouragement and unceasing prayers. Special thanks to Dr. Jesse
Kang’ethe Mukuria, Mr. John Munguti Kirumba and Mr. Douglas Ngugi Mbugua for
their enormous financial and moral support in my studies and without whom i wouldn’t
have reached this far. Lastly, i appreciate Evans Odongo for his assistance in getting data
from SASRA.
iv
DEDICATION
This research project is dedicated to my esteemed wife Joyce Nyambura and my two sons
Joel and Cyprian for their motivation, love and support during this study.
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TABLE OF CONTENTS
DECLARATION........................................................................................................................... ii
ACKNOWLEDGEMENTS ........................................................................................................ iii
DEDICATION.............................................................................................................................. iv
LIST OF TABLES ..................................................................................................................... viii
LIST OF FIGURES ..................................................................................................................... ix
LIST OF ABBREVIATIONS ...................................................................................................... x
ABSTRACT .................................................................................................................................. xi
CHAPTER ONE ........................................................................................................................... 1
1.1 Background of the Study .................................................................................................................... 1
1.1.1 Tax Incentive ................................................................................................................................... 2
1.1.2 Financial Performance ..................................................................................................................... 3
1.1.3 Relationship between Tax Incentive and Financial Performance .................................................... 4
1.1.4 SACCOs in Nairobi County ............................................................................................................. 5
1.2 Research Problem. .............................................................................................................................. 5
1.3 Research Objective ............................................................................................................................. 7
1.4 Value of the Study .............................................................................................................................. 7
CHAPTER TWO .......................................................................................................................... 9
2.1 Introduction ......................................................................................................................................... 9
2.2 Theoretical Literature .......................................................................................................................... 9
2.2.1 Corruption and Influence Theory ................................................................................................. 9
2.2.2 Agglomeration Economies Theory. ............................................................................................... 10
2.2.3 Neo-classical Theory. .................................................................................................................... 11
2.3 Determinants of Financial Performance of SACCOs ....................................................................... 12
2.3.1 Size of the Firm .............................................................................................................................. 12
2.3.2 Age of the Firm .............................................................................................................................. 13
2.3.3 Liquidity of the Firm ...................................................................................................................... 13
2.3.4 Capital Assets Management Earnings Liquidity (C.A.M.E.L) ...................................................... 14
2.3.5 Operating Profit Margin (OPM) & Asset Turnover Ratio (ATR) ................................................. 14
2.4 Empirical Studies .............................................................................................................................. 15
2.4.1 International Studies .................................................................................................................. 15
2.4.2 Local studies .................................................................................................................................. 16
2.5 Conceptual Framework. .................................................................................................................... 17
Figure 2.1 Conceptual Framework ............................................................................................... 18
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2.6 Summary of the Literature Review ................................................................................................... 18
CHAPTER THREE .................................................................................................................... 20
3.1 Introduction ....................................................................................................................................... 20
3.2 Research Design ................................................................................................................................ 20
3.3 Target Population .............................................................................................................................. 20
3.4 Sample Size and Sampling Techniques ............................................................................................ 20
3.5 Data Collection ................................................................................................................................. 21
3.6 Data Analysis .................................................................................................................................... 21
3.7 Test of Significance/ Diagnostic Tests .............................................................................................. 22
3.7.1 Normality Test ............................................................................................................................... 22
3.7.2 Autocorrelation Test ...................................................................................................................... 22
3.7.3 Heteroscedasticity Test .................................................................................................................. 22
3.7.4 Multicollinearity Test ..................................................................................................................... 23
CHAPTER FOUR ....................................................................................................................... 24
4.1 Introduction ....................................................................................................................................... 24
4.2 Diagnostic Tests ................................................................................................................................ 24
4.2.1 Normality Test ........................................................................................................................... 24
4.2.2 Heteroskedasticity Test .............................................................................................................. 25
4.2.3 Multicollinearity Test ................................................................................................................. 25
4.2.4 Autocorrelation Test .................................................................................................................. 26
4.3 Descriptive Statistics ......................................................................................................................... 26
4.4 Correlation Analysis ......................................................................................................................... 27
4.5 Regression Analysis .......................................................................................................................... 27
4.6 Discussion of Findings ...................................................................................................................... 29
CHAPTER FIVE ........................................................................................................................ 30
5.1 Introduction ....................................................................................................................................... 30
5.2 Summary of Findings ........................................................................................................................ 30
5.3 Conclusion ........................................................................................................................................ 31
5.4 Recommendations ............................................................................................................................. 31
5.5 Limitations of the Study .................................................................................................................... 31
5.6 Suggestions for Further Research ..................................................................................................... 32
REFERENCES ............................................................................................................................ 33
APPENDICES ............................................................................................................................. 38
Appendix 1: Data collection sheet .......................................................................................................... 38
vii
Appendix 11: List of Registered SACCOs ............................................................................................. 39
Appendix 111: Research Data................................................................................................................. 41
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LIST OF TABLES
Table 4.1: Shapiro Wilk W Test for Normal Data……………………………………..24
Table 4.2: Heteroskedasticity Test……………………………………………………..25
Table 4.3: Variance Inflation Factor…………………………………………………...25
Table 4.4: Autocorrelation Test………………………………………………………..26
Table 4.5: Descriptive Statistics……………………………………………………….26
Table 4.6: Correlation Matrix………………………………………………………….27
Table 4.7: Regression Analysis………………………………………………………...27
Table 4.8: Analysis of Variance………………………………………………………..28
Table 4.9: Regression Model Coefficients……………………………………………..28
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LIST OF FIGURES
Figure 2.1 Conceptual Framework ........................................................................................... 17
x
LIST OF ABBREVIATIONS
ATR- Asset Turnover Ratio
C.A.M.E.L- Capital Assets Management Earnings Liquidity
CBD- Central Business District
EPZ- Export Processing Zone
EU- European Union
FDI- Foreign Direct Investment
FOSA- Front Office Services Activity
FWD- Farm Work Deduction
GDP- Gross Domestic Product
IBD- Industrial Building Deduction
ID- Investment Deduction
KRA- Kenya Revenue Authority
MUB- Manufacturing Under Bond
NSE- Nairobi Security Exchange
OPM- Operating Profit Margin
R & D- Research and Development
ROA- Return on Asset
ROE- Return on Equity
SACCOs- Savings and Credit Cooperative Societies
SASRA- Saccos Societies Regulatory Authority
TREO- Tax Remissions and Exemptions Office
UNCTAD- United Nations Conference on Trade and Development
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ABSTRACT The purpose of this study was to establish the effects of tax incentives on financial
performance of SACCOs in Nairobi County. The study adopted a descriptive research
design. The study population comprised of all the registered SACCOs in Nairobi County.
A sample of 41 SACCOs was determined using 10- 30% of target population as
representative rule and stratified random sampling technique. Secondary data from
SASRA was collected and analyzed to establish the association between tax incentives
and profitability of SACCOs. This study established that there is a weak positive
relationship between capital allowance, accelerated depreciation and financial
performance of SACCOs in Nairobi County. It further indicated a negative relationship
between tax and financial performance. The study therefore recommended that the
government should provide more and a diversity of tax incentives to the SACCOs,
especially capital allowance and accelerated depreciation and tax exemptions, since an
increase in each of them increases the profitability of SACCOs.
1
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study.
SACCOs are regarded globally as key players in economic growth of a majority of
countries. Vibrant and dynamic cooperative sectors contribute largely to the economic
development of different countries. SACCOs in Kenya actively contribute to 30% of the
economy, this is possible because they are well distributed all over (Karagu & Okibo,
2014). It is through SACCOs that many Kenyans save their funds for investment in
businesses and home ownership further contributing to Kenya’s Vision 2030
development agenda. In an attempt to stimulate growth and good financial performance
of SACCOs, tax incentives are offered to motivate them to increase their investments
(Mintz & Chen, 2011). In countries like Russia for example, tax incentives are a useful
tool for increasing the profitability and improving the financial performance of
businesses (Markov, 2016). Among the EU policy makers, tax incentives are a commonly
used threshold to slightly reduce the tax liability of firms.
The study focused on corruption and influence, agglomeration economies and neo-
classical theories. According to corruption and influence theory, countries with weak tax
laws and firms involved in bribing government leaders enjoy illegal high levels of tax
incentives. Agglomeration of economies theory which was developed by Garcia- Mila
and McGuire explains that, firms which are located in the cities have got many skilled
workers than their counterparts in rural areas and thus they receive high tax incentives.
Neo- classical theory which was developed by Solow in 1956 indicates that there should
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be no inequality in the distribution of tax incentives, all qualified firms should be
provided with tax incentives.
In Kenya, tax incentives are categorized into export or investment promotion incentives.
Export promotion incentives comprises of Export Processing Zone (EPZ), the Tax
Remissions and Exemptions Office (TREO) and Manufacture Under Bond (MUB). The
aim of EPZ is to Promote FDI, while TREO and MUB were meant to promote
manufacture of goods in the country for export. Investment Promotion Incentives include
Investment Deductions (ID) which is for processing machines, Industrial Building
Deductions (IBD) which is granted on factory buildings and staff quarters, Mining
Allowance which is granted on Mining and Farm Work Deductions (FWD) which is
usually granted on capital expenditure incurred in farming (Githaiga, 2013). Despite the
significance of stability of SACCOs, very scarce research on SACCOs and tax incentives
is available.
1.1.1 Tax Incentive
According to James (2013) tax incentive is the process of reducing the tax liability which
is available on various investments, through deviations from the unnecessary taxes levied
in the system. It is provision for taxes that is made available for SACCOs to decrease the
tax liability. Tax incentive is characterized by accelerated depreciation, tax credits and
reductions in tax rates/exemptions and capital allowance.
Depreciation generally in finance and accounting, is all about assets losing their value
over time or experiencing a reduction in their useful life. According to
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Koowattanatianchai, Charles and Eddie (2009) accelerated depreciation incentive is the
reduction in the useful life, or deterioration of a firm’s asset cost in a faster manner in its
beginning years than in years later. The advantage of this incentive is confined to deferral
of tax. Since firms submit taxes on their surplus which are derived from deducting the
expenses, accelerated depreciation being an expense, defers taxes of a firm especially in
initial years of the life of an asset and boost them in future years. This incentive leads to
more present value of the depreciation deductions and thereby increases the after-tax
present value of the net returns. It is like a loan given to taxpayers that has no interest but
a deferred tax payment.
Lower or reduced tax rates encourage and enable investors and firms to operate the
businesses and open new ventures. Capital allowance is lowering a given investment
percentage from the surplus which are taxable, it is usually granted to cater for the usage
of assets in the business (Daniel & Faustin, 2019). It is normally granted on capital
expenditures incurred in the business. Tax credits are reductions of tax burden and are
traditional methods which stimulate investments and innovations, but in relation to small
business they are specific depending on the country where they are applied.
1.1.2 Financial Performance
The general health of a company over a given time span is regarded as financial
performance (Bhunia, Mukhuti & Roy, 2011). It is an analysis on the appraisal of growth
of a firm. Most of the SACCOs rely on financial performance to evaluate the success and
proper business governance. However, few existing indicators will be used by SACCOs.
Some of the financial performance indicators used are tools that determine the revenue of
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businesses and allow them to fulfill the given obligations. (Arcenildo Valderes Da Silva
Nunes, 2012)
The trend analysis of various SACCOs in Kenya indicate that they had a good
development in capital, loans, deposits and total assets. (Buluma, Kung’u, & Mungai,
2017). In this study financial performance was determined using ratios such as firm size,
firm growth, liquidity and Capital Assets Management Earnings Liquidity (C.A.M.E.L)
among others which were derived from the statements of businesses.
1.1.3 Relationship between Tax Incentive and Financial Performance
Countries have always been using tax incentives to benefit businesses and protect them
from failure in order to influence their expansion and start-up. Zee, Stotsky and Ley
(2002) argues tax incentive programs focus on creating employment and attract
productive investment for the firms. However, a study by UNCTAD (2004) points out
that, some countries provide conditions before granting tax incentives. For example, in
the case of FDI, they demand the investors to create employment opportunities, to
transfer a particular technology or to establish their firms in specific regions in the
country. Daniel and Faustin (2019) indicates that governments create obstacles in the
business environment and investment. Tax incentives are thus used to offer compensation
for high tax rates through exemption and reduction in taxes.
In Kenya, newly listed companies enjoy reduced tax incentives of less than 30%
corporate tax rate for the first few years of operation (Tirimba, Muturi, & Sifunjo, 2016).
This is important to the firms, since their net revenue will increase leading to good
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financial performance. Jun (2018) cautions that some countries have the habit of
distorting resources allocated to businesses, enacting complex tax laws and providing
opportunity for corruption thereby affecting the revenues of the firms.
1.1.4 SACCOs in Nairobi County
SACCOs are institutions or cooperatives registered under the cooperative societies Act
which are mandated to collect deposits, savings and advancement of loans to members to
promote their welfare (UN-Habitat, 2010). They are vital in supporting investors and
those who require capital to establish businesses. In Kenya, SACCOs are found in almost
all the counties and they provide access to funds, savings mobilization and wealth
creation to citizens. SACCOs in Nairobi County are adopting new approaches and model
therefore responding to different financial environment. (Buluma et al, 2017).
Most cooperative societies in Nairobi County are SACCOs. They provide services to
members such as Front Office Services Activity (FOSA), emergency loans, special loans,
development and college/school fees loans. According to Waswa (2013) SACCOs are
legislated through a Societies Act of 2008 that provides licensing, regulations,
supervision and promotion of SACCOs by a regulatory authority of SACCOs societies
called SASRA. The Kenyan Government should look into incentivizing SACCOs growth
especially in the county government of Nairobi. Furthermore, this will improve their
financial performance and empower them to invest more.
1.2 Research Problem.
SACCOs in Kenya were performing poorly in the past few years leading to some of them
collapsing with member’s deposit, this was attributed to many difficulties like heavy
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taxation, stiff competition, poor corporate governance and instability (Chahayo, Bureti,
Juma, Maende & Aketch, 2013). This challenges made them to contribute negatively to
the economy of the country. SACCOs worldwide are significant channels of economic
prosperity, they play a vital role in granting finances to the needy (Karagu & Okibo,
2014). However, reports reveal that attaining proper utilization of tax incentives by many
nations to support firms like SACCOs remain an unrealized dream (Feyitimi, Temitope,
Akeem, & Samuel, 2016). Some governments are faced with fiscal indiscipline towards
firms due to their inability to give tax incentives, charging multiple taxes, introducing
new levies and difficulty in effectively implementing tax policies.
Research has been undertaken on tax incentives globally and locally. However, little
research has been undertaken on tax incentives and its effects on SACCOs. A research
was done on how policy makers from poor countries benefit from using tax incentives
(Zee et al, 2002) the variables used were not exhaustive. Feyitimi et al, (2016) undertook
a study in Nigeria about contribution of tax incentives from the government to growth of
SME’s, but the research was done in a different developing country. In Uganda Mayende
(2013) analyzed how the performance of manufacturing industries is affected by tax
incentives in regards to value added and gross sales. But he only considered the
manufacturing firms.
Onyango (2015) conducted a study in Nairobi County on how performance of Five star
hotels is affected by tax incentives, that research revealed conflicting findings as IBD and
ID negatively affected financial performance whereas W&T had a positive effect on the
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hotels. Gumo (2013) sought to establish how FDI in Kenya is affected by tax incentives,
though he didn’t consider capital allowance which is an important part of tax incentives
to be analyzed under the study. Murage (2012) investigated how the EPZ firms in Kenya
are affected by tax incentives especially in Business Investment, the research however
revealed that the impact on the dependent variable is not significant. Lack of
inconsistency and adequate empirical literature in the results of the previous studies
necessitates further research to be conducted. While the above researchers made great
contributions, this study thus deliberate on establishing if actually the profitability of
SACCOs is affected by tax incentives.
1.3 Research Objective
To determine effects of tax incentives on financial performance of savings and credit
cooperative societies in Nairobi County.
1.4 Value of the Study
Through comprehending the need for receiving and giving tax incentives to SACCOs, the
government which is usually interested in tax matters especially of various enterprises
will benefit from this study. It is through this study that the government will know the
response of the firms which enjoy tax incentives and the measures to take.
This research will be a stepping stone and a source of relevant information into the
operations of SACCOs, as it will assist the scholars and researchers to analyze the
literature that involves tax incentives and performance of a large number of SACCOs and
therefore, adopt the outcome of these findings to do more research which will definitely
benefit the SACCOs.
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This study will also act as a guide to the policy makers and stakeholders. It will enable
the management and administration of various SACCOs in implementing and executing
the recommendations such as, appropriate tax incentive laws and regulations. This will
promote and improve both the SACCOs’ financial and economic growth in Kenya as
decisions on tax incentives and investing in implementation of SACCOs will be fulfilled,
guided and reviewed by the research findings (Karagu & Okibo, 2014)
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CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This part analyzes theoretical, empirical literature and conceptual framework of what
other researchers have done.
2.2 Theoretical Literature
In this area, theories supporting the study are covered. They include corruption and
influence theory, Agglomeration economies theory and Neo classical theory.
2.2.1 Corruption and Influence Theory
Corruption is misuse of public power by breaking down the set rules and regulations for
self-interest (Jain, 2011). In this theory, tax incentive in most cases don’t represent or
indicate tax revenue maximization. Instead, it reflects the ability of the firms to bribe
leaders in a given government. Pani and MPani (2009) points out that, corruption can be
reduced by offering proper political and economic incentives which are appropriate to the
firms.
The theory contributes to the occurrence of tax incentives, some firms get tax reductions
and tax exemptions through bribing and coercing government and tax officials. The
percentage of tax incentive given to most firms determined by their capability to bribe.
Pellegrini and Gerlagh (2004) adds that if public institutions such as political parties,
legal system, civic groups and the media become weak and ineffective they definitely
cannot be able to expose the corrupt. Corruption and very high tax incentives leads to
10
negative net revenues of a country, poor allocation of resources, poor financial
performance of firms and poor economic growth.
According to this theory, tax incentives are higher in countries which have a weaker rule
of law. Furthermore, tax revenue collected is not properly used to serve the purpose of
the firms. For instance in USA in the 19th
and 20th
century, various railroad companies
were involved in bribing politicians in return for generous tax incentives (Glaeser, 2001).
According to Imam and Jacobs (2007) complex tax laws and systems can contribute to
corruption in a country. Moreover, tax officials should be well paid, well trained, well
oriented, competent and above all have high integrity when operating or handling tax
issues. This will help prevent corruption in distribution of tax incentives to SACCOs.
2.2.2 Agglomeration Economies Theory.
This theory is a representation of contribution of literature of Garcia- Mila and McGuire.
They indicated that cities prefer to have many firms in a situation where agglomeration
economies are present. These cities are mainly concerned in getting high spillovers from
the firms and in return offer these firms high tax incentives. Nevertheless, the type and
location of a firm determines a lot whether it will have a high spillover or not, cities
which have got skilled workers and are located in marketable areas where they receive
specialized services tend to get high spillovers (Agarwalla, 2011).
Agglomeration economies therefore suggests tax incentives will be most in firms that
have a large number of skilled laborers. Glaeser (2001) argues that agglomeration of
economies becomes more sensible in cities with many firms which are growing rapidly
11
than in those with just a few number of big firms, because, it becomes easy to rationalize
tax incentives for small firms as opposed to big firms. Agglomeration tax incentives are
also given to firms which are divers in terms of their operations leading to a liquidity
increase.
According to this theory, a firm that is located in a good spot in the city and is able to
attract other firms into that city, will definitely be rewarded with high tax incentives.
However, the shortcoming of this theory is that firms located away from the city will be
rewarded with little or no tax incentives. The location and performance of firms is
indirectly affected by agglomeration economies which though do not contribute to their
economic growth (Neumark, Junfu & Wall, 2006). Garcia-Mila and McGuire in this
theory prove that, the net tax payments of the public services are likely to become
negative due to high tax incentives.
2.2.3 Neo-classical Theory.
This theory dwells on labour, technology and capital. It was developed by Solow (1956)
who articulates that the population growth rate and the technical progress plays a critical
role in a government’s long run growth rate. This theory mostly focuses on the human
beings in an organization. Though taxation interferes with the incentive to invest in
human or business capital.
This theory further posits that a good organization is that in which there is a combination
of informal and formal sectors. It advocates for low tax rates, tax incentives and limited
government spending for firms so that they may flourish and perform well financially.
Colmar (2005) indicates that tax incentives offers many benefits like compensation for
12
losses in investments and symbolic signaling effects. Tax cut also causes a rise in labor
supply as the workers will be able to increase their work efficiency, effectives and
working hours. The government will be able to increase its tax revenue, because due to
low tax rates the firms will submit their taxes effectively and thus tax evasion and tax
avoidance will be a thing of the past.
In Neo-classical economic theory, a tax system of horizontal equity to the investors is a
‘good tax system’ and it prevents prejudice in the provision of tax incentives (Barbour,
2005). Furthermore, the presence of inequality in distribution of tax incentives in
particular sectors will discourage investors, and lead to a drop in growth.
2.3 Determinants of Financial Performance of SACCOs
Every business aspires to improve its competitive position in the market through
analyzing its financial position using certain determinants (Bhunia et al, 2011).
Determinants of financial performance to be discussed in this section are; the size,
growth, liquidity, Capital assets management earnings liquidity, ATR and OPM firms.
2.3.1 Size of the Firm
According to Serrasqueiro and Maçãs Nunes (2008) a firm size is considered to be the
number of assets and employees it has and the total turnover available. Taxation is a
major hindrance to growth of a firm, tax incentives therefore are important in improving
a firm’s output. Mayende (2013) conducted a study which suggested a strong correlation
between large sized firms and revenue compared to small and medium sized ones. This is
a clear indication that large and medium sized firms can maximize their profits due to
their efficiency and effectiveness, therefore, increase their returns leading to a better
13
future profitability (Ondoro, 2015). Serrasqueiro and Maçãs Nunes (2008) concluded that
despite the measure of the size used, performance and size of the firms are positively and
significantly related. The fact that large firms have more qualified laborers and can easily
access credit for investment, makes them perform financially better than small firms.
2.3.2 Age of the Firm
Age and size are important factors for the growth of young SACCOs. An increase in age
of a firm doesn’t affect the relations between its size in the past and growth in the present
period (Voulgaris, Asteriou, & Agiomirgianakis, 2003). Young SACCOs will tend to
uplift their growth rate in order to reach a particular size and secure their survival, but
once they attain survival their rate of growth reduces. While age and size of firms
contribute to a declining growth in young SACCOs, labour and high production
positively influences the growth of young firms. Both young and old firms experience the
diminished growth caused by the intensity of R&D.
2.3.3 Liquidity of the Firm
The effectiveness and ability in the transfer of a firm’s assets into cash is the liquidity of
a firm, it determines a firm’s strength in meeting the short term liabilities such as
financial and operating expenses occurring in a firm in a short span (Durrah, Rahman,
Jamil & Ghafeer, 2016). Liquidity ratios are characterized by cash ratio, current ratio and
return on assets ratio which are mainly used by companies to manage and improve their
financial performance.
ROA determines how the firm uses its assets to generate profits, and ROE is a liquidity
ratio that indicates the return for every one shilling of equity capital contributed by
shareholders of a firm. ROE is derived from net profit after tax or net income against the
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shareholder’s total equity. Ryan, Robinson and Grigg (2000) revealed that current ratio is
the best known determinant of liquidity as it is obtained from current assets against
current liabilities.
2.3.4 Capital Assets Management Earnings Liquidity (C.A.M.E.L)
According to Derviz and Podpiera (2008) CAMEL is a supervisory system of rating and
analyzing the overall condition of firms, it is a generally accepted measure of the safety
of firms which is applied to credit institutions. Individual results from financial reports of
various companies is often compared to CAMEL in order to identify or determine any
arising crisis. Capital adequacy is important in determining how strong firms can
withstand poor financial statements. Asset quality analyzes the strength of firm in
comparison to the depreciation of the assets. Management is all about compliance with
the set rules and regulations, leadership and administrative ability. Strong earnings of
firms indicate the ability to support operations of a firm.
2.3.5 Operating Profit Margin (OPM) & Asset Turnover Ratio (ATR)
It is derived from the output, in the available resources of a firm, OPM and ATR are the
main indicators of income that have an influence on firms. An increase in either or both
of them will result to an improved financial performance. The ROA of firms can also be
derived from the product of OPM by ATR. Businesses which experience poor operating
profit margin, can improve their financial performance by implementing cost controls of
their operations.
15
2.4 Empirical Studies
Research both at local and international level has been done to investigate the impacts tax
incentives have on performance indicators. Therefore, this section reveals a literature
study from different researchers.
2.4.1 International Studies
Klemm and Parys (2009) researched on effects of tax incentives in encouraging
investments as well as a tool of tax competition. The study was done between 1985 and
2004 in more than 40 Caribbean and Latin American countries. The study found that tax
holidays had strategic interactions, however, no evidence was found for competition over
tax credits. The research also concluded that FDI is attracted by longer tax holiday. A
study was done on why tax incentives are important to policy makers. It was ascertained
that they should only be for correcting firms which experience market failures and that,
investments can only be recovered faster through implementing the preferred form of tax
incentives (Zee et al, 2002)
In a targeted population represented by a sample of 100 businesses in Osun State
Industrial area in Nigeria, Feyitimi et al, (2016) assessed the role various governmental
tax incentives play in contributing to the growth of SMEs. Primary data was retrieved
through interviews, observations and questionnaires. The research design used was
descriptive. The study revealed a correlation of taxation and SME’s growth.
In Uganda a study was done to analyze what effects tax incentives had on the profitability
of manufacturing industries. Panel data technique was used in the research. The results
16
were that tax incentives triggers industries to perform well in sales (Mayende, 2013). The
study recommended the government to implement policies such as, technical skills in
development for proper utilization of available tax incentives for better performance of
firms.
2.4.2 Local studies
A research on Five-star hotels’ performance in relation to tax incentives was done in
Nairobi County, the hotels represented the target population. Questionnaires were used to
conduct a censor survey. The research concluded that IBD and ID negatively affected
financial performance whereas W&T had a positive effect. Therefore the study
recommends that W&T tax incentives should be encouraged by the government because
they are beneficial in improving the performance of hotels. (Onyango, 2015).
A survey on how FDI is affected by tax incentives was done in Kenya where-by Gumo
(2013) collected secondary data from KRA, KNBS and the treasury and used a research
design that was descriptive. The study found that on investment incentives, both mining
operation deduction and investment deduction positively affect FDI, while industrial
allowance negatively affect FDI. The study therefore concludes that, FDI is positively
affected by tax incentives.
Ngure (2018) studied how firms in the manufacturing sector in Kenya are affected by tax
incentives. The target population was 725 firms. The sample size was made up of 90
firms. Secondary data obtained was from 2011 to 2016. It was established that excise,
17
custom duty and capital allowance incentives positively affected performance of
companies. The study findings recommended the need and expansion of tax incentives.
Murage (2012) investigated how EPZ firms are affected by tax incentives especially in
Business Investment in Kenya, 104 EPZ firms in Kenya made up the target population,
where the researcher selected 65 firms located in Nairobi Metropolitan. It was discovered
that an increase in profits, sales and tax incentives is likely due to an increase in
investment in EPZ firms.
2.5 Conceptual Framework.
A manifestation of association between all variables is referred to as a conceptual
framework (Mugenda & Mugenda, 2003). In this research, independent variables are the
various tax incentives like accelerated depreciation and reductions in tax rates among
others. Whereas the dependent variable is financial performance.
18
Figure 2.1 Conceptual Framework
Independent Variables
Tax Incentives
Dependent Variable
Financial Performance
Control
Variable
Source: Author (2019)
Based on Figure 2.1. Further capital allowance as an independent variable is considered
as the amount of money which can be deducted from the firm’s profits. While
performance is considered on the effectiveness of the SACCOs’ operations leading to
their proper growth.
2.6 Summary of the Literature Review
Theoretical review dwelled on theories such as corruption and influence theory,
agglomeration economies theory and Neo- classical theory. All this theories propagate
that tax incentives are essential to firms. In the empirical review findings above, the
Accelerated
Depreciation
Tax rates
Capital Allowance
Financial
performance of
firms determined by
ROA
Size of the firm
19
results of various studies have clearly indicated how positively the profitability of firms is
affected by tax incentives.
Research has been conducted internationally and locally to analyze the impacts of tax
incentives on certain dependent variables such as FDI, investments and performance of
firms in different industries as indicated above, global studies in both developed and
developing economies have also been done in this area by others like Klemm and Parys
(2009) and Zee et al (2002) in developed economies. Feyitimi et al (2016) and Mayende
(2013) in regional developing economies and in the local developing setting Murage
(2012), Gumo (2013), Onyango (2015) and Ngure (2018) among others. However, of all
these studies, none has studied tax incentives and their effects on profitability of
SACCOs. This study thus investigate the effects of tax incentives on financial
performance of SACCOs.
20
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Introduction
A research methodology is a detailed procedure that describes systematically how a
research problem will be solved.
3.2 Research Design
A research design is a set-up of requirements for obtaining data and analyzing it such that
the purpose of research is relevant to existing economy (Kothari, 2004). The research was
conducted through a descriptive research design which is suitable because it uses smaller
samples for an in-depth analysis of the larger population that was studied. Furthermore, a
descriptive design is better as it describes and explain a phenomenon rather than predict
it. It gives a holistic explanation of the way variables relate in the study.
3.3 Target Population
Alvi (2016) indicates that all members who qualify and meet a given criteria that is
specified and necessary for research investigation as the target population. There are 175
SACCOs licensed by SASRA to operate in Kenya. The population target comprised of 41
licensed SACCOs in Nairobi County.
3.4 Sample Size and Sampling Techniques
A sample is a section of entities which is part of population that determines and
approximates the features of the population. (Crammer & Howitt, 2004). This study
employed a stratified random sampling technique, which identify sub-groups in the
population that have got same features and form a sample from these sub-groups. A total
21
of 41 SACCOs was identified and chosen from the target population. Mugenda and
Mugenda (2003) propose for 10-30% of target population as representative. The
researcher chose to study all the 41 SACCOs based in Nairobi County, representing
23.43% of the entire target population.
3.5 Data Collection
Gall, Gall and Borg (2007) describes data collection as the process or procedure of
collecting and gathering raw information that is unprocessed but which can be processed
into vital information, after being scientifically analyzed. Secondary data was collected
and utilized in this research. Data was retrieved from the annual audited financial
accounts and published financial SASRA reports of the SACCOs. The data was for a
span of five years, ranging from 2014 to 2018.
3.6 Data Analysis
Data analysis is a strategy of decreasing and arranging data to give results which will be
properly interpreted by the one performing the research (Burns & Grove, 2003). Stata
software was used to analyze and interpret the data. Correlation Analyses helped to
examine the difference between variables, while multiple regression analysis examined
the significance of existing relationship between them. The results were interpreted in
tables.
The regression model applied for data analysis was:
Y= β0 + β1 X1 + β2X2 + β3X3 + ԑ
Where;
Y = Financial Performance (Measured by ROA)
X1 = Accelerated Depreciation (Annual depreciation on total assets)
22
X2 = Tax rates (Tax on total revenue)
X3 = Capital Allowance (Capital allowance against the total assets)
β0 = Constant term
β1 to β3 = Beta coefficients
ԑ = Error term
3.7 Test of Significance/ Diagnostic Tests
The research was carried out on the 41 registered SACCOs whereby normality,
autocorrelation, heteroscedasticity and multicollinearity tests were carried out.
3.7.1 Normality Test
The normality of the variables was determined using Shapiro Wilk W test. The data is
declared as normally distributed if the P-test of the variables becomes more than the
significant level of 5% or 0.05,
3.7.2 Autocorrelation Test
To test for Autocorrelation, Durbin-Watson d stastic (dwatson- time series only) test was
used. The rule of thumb is if it is near or equal to 2, then the data has autocorrelation.
Incorrect estimates and biased errors exist if the researcher fails to recognize them.
3.7.3 Heteroscedasticity Test
It was done using Breusch-Pagan/Godfrey. The rule is that if the P-test is more than 5%
or 0.05 confidence level it becomes insignificant, meaning that there will be
homoscedasticity thus no heteroscedasticity.
23
3.7.4 Multicollinearity Test
In the event where more than two variables are highly correlated, multicollinearity is
bound to occur. The presence of multicollinearity for each independent variable was
tested through Variance Inflation Factor (VIF). To determine severe multicollinearity,
VIF should be equal to or greater than 5. If it is less than 5 then it implies that all the
variables indicated absence of multicollinearity (Hair et al, 2010).
24
CHAPTER FOUR
DATA ANALYSIS, RESULTS AND DISCUSSION
4.1 Introduction
Research findings were presented in this chapter. The study period was 5 years,
secondary data for years 2014 to 2018 was obtained. Analysis was done on the data and
results were presented in tables.
4.2 Diagnostic Tests
4.2.1 Normality Test
Table 4.1: Shapiro Wilk W Test for Normal Data
Source: Research Findings
Ho: The data is normally distributed.
In table 4.1 above, Shapiro Wilk W Test was done, the P-test of ROA is 0.00000; that of
Capital allowance is 0.00013; that of Tax is 0.00000 while that of Accelerated
depreciation is 0.00000, they are all less than the significant level of 5% or 0.05, since
they are all significant, you reject the null hypothesis. Thus the data of all the variables is
not distributed normally.
Shapiro-Wilk W test for normal data
Variable Obs W V z Prob>z
ROA 41 0.51845 19.400 6.250 0.00000
CapitalAll~e 41 0.85978 5.649 3.649 0.00013
Tax 40 0.71703 11.185 5.081 0.00000
Accelerate~n 41 0.42042 23.350 6.640 0.00000
25
4.2.2 Heteroskedasticity Test
Table 4.2: Heteroskedasticity Test
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of ROA
chi2(1) = 6.78
Prob > chi2 = 0.092
Source: Research Findings
Using Breusch –Pagan test, table 4.2 indicates the results. The H0 indicates that there is
constant variance. Therefore, our H0 is that there is no heteroskedasticity due to the
constant variance, but the P-test is 0.092 which is more than 5% or 0.05 confidence level,
it is insignificant, therefore you do not reject the H0, thus there is no heteroskedasticity.
4.2.3 Multicollinearity Test
Table 4.3: Variance Inflation Factor
Variable VIF 1/VIF
Accelerate~n 1.04 0.961618
Tax 1.04 0.963156
CapitalAll~e 1.03 0.972200
Mean VIF 1.04
Source: Research Findings
The rule of thumb to determine severe multicollinearity is that; VIF should be equal to or
greater than 5. In the table 4.3 above, our VIF test shows a mean VIF of 1.04 which is
less than 5, again all the variables presented a VIF test of less than 5. This implies that all
the variables depicted absence of multicollinearity.
26
4.2.4 Autocorrelation Test
Table 4.4: Autocorrelation Test
Number of gaps in sample: 1
Durbin-Watson d-statistic( 4, 40) = 1.835491
Source: Research Findings
To test for autocorrelation, we used Durbin-Watson d statistic (dwatson- time series
only). The results are as shown in table 4.4 above. The rule is, if the test is near or equal
to 2, reject the null hypothesis. Our test is 1.835491 which is near to 2. It therefore means
that our model has no autocorrelation.
4.3 Descriptive Statistics
Findings in table 4.5 showed the descriptive statistics on ROA, Capital allowance, Tax
and Accelerated depreciation incentives of SACCOs.
Table 4.5: Descriptive Statistics
Variable Obs Mean Std. Dev. Min Max
ROA 41 .3526341 .6474611 .016 3.031
CapitalAll~e 41 .0324878 .0293523 .001 .099
Tax 40 .796975 .8522681 .008 3.714
Accelerate~n 41 .0644146 .1420359 .002 .739 Source: Research Findings
In table 4.5 above the mean for ROA was 0.3526341, capital allowance had a mean of
0.0324878, tax and accelerated depreciation had a mean of 0.796975 and 0.0644146
respectively. ROA had a standard deviation of 0.6474611, capital allowance had a
standard deviation of 0.0293523, tax and accelerated depreciation had a standard
deviation of 0.8522681 and 0.1420359 respectively
27
4.4 Correlation Analysis.
Table 4.6: Correlation Matrix
ROA Capita~e Tax Accele~n
ROA 1.0000
CapitalAll~e 0.1578 1.0000
Tax -0.0860 -0.1052 1.0000
Accelerate~n 0.2448 -0.1124 -0.1477 1.0000
Source: Research Findings
From table 4.6 there’s a weak positive correlation between capital allowance and ROA as
indicated by the coefficient factor of 0.1578. The results also revealed a negatively weak
correlation between tax paid and ROA as depicted by a factor of -0.0860. Lastly the
results showed a weak positive correlation between accelerated depreciation and ROA as
indicated by a factor of 0.2448. This clearly indicates a negative relationship between tax
paid and profitability of SACCOs and a positive relationship between capital allowance
and accelerated depreciation on financial performance of SACCOs in Nairobi County.
4.5 Regression Analysis
Multiple regression analysis was done using Stata.
Table 4.7: Regression Analysis
R = 0.3004
R-squared = 0.505
Adj R-squared = 0.201
Root MSE = .6482
Source: Research Findings
The results in table 4.7 above show R squared is 0.505 which means 50.5% of variation
of financial performance are explained by tax incentives. The correlation coefficient R
28
depicts what relationship exist between the research variables. The results above show a
positive relationship existing among the variables as outlined by a figure of 0.3004.
Table 4.8: Analysis of Variance
Analysis of Variance
Source SS df MS F Prob > F
Between groups 16.0745398 31 .518533543 6.73 0.0025
Within groups .693693667 9 .077077074
Total 16.7682335 40 .419205838 Source: Research Findings
In table 4.8 above, the sum of square for regression or variables between groups was
16.0745398; f statistics value was 6.73. The probability significance was 0.0025 which is
below the significance level of 0.05 and which shows tax incentives influence
performance.
Table 4.9: Regression Model Coefficients
ROA Coef. Std. Err. t P>t [95% Conf. Interval]
CapitalAllowance 4.190404 3.655661 1.15 0.259 3.223621 11.60443
Tax -.0214859 .1240941 -0.17 0.864 .2731603 .2301886
AcceleratedDepre
ciation
1.190199 .736046 1.62 0.115 .3025713 2.68297
_cons .1663761 .203633 0.82 0.419 .2466108 .579363 Source: Research Findings
According to table 4.9 above, the established regression model was;
Y= 0.1663761 + 4.190404X1- 0.0214859 X2 +1.190199X3
With the independent variables at a constant, the model above shows a unit increase in
capital allowance and accelerated depreciation would lead to an increase in performance
by 4.190404 units and 1.190199 units respectively. Whereas a unit increase in tax would
lead to a decrease in performance by 0.0214859 units.
29
4.6 Discussion of Findings
The test for normality on all the variables, gave a P-test for each of them which was less
than the significant level of 5% or 0.05 an indication the data is not distributed normally.
On testing heteroscedasticity, the P-test was more than 5% or 0.05 confidence level,
which means there was no heteroskedasticity. From Multicollinearity test, the VIF test
showed a mean VIF which was less than 5. This revealed that all the variables depicted
absence of multicollinearity.
From the study on correlation analysis, the results showed capital allowance and
accelerated depreciation positively influenced the profitability of SACCOs. This means
when capital allowance and accelerated depreciation increases so does the profitability of
SACCOs. This findings are in agreement with the research findings of Ngure (2018)
which found that capital allowance positively influence the financial performance of
manufacturing firms in Kenya. However, the research results showed that tax negatively
affected the performance of SACCOs. An increase in tax charged on SACCOs would
lead to a decline in their profitability, this results are similar to those of Kinyua (2015)
who studied the effects of turnover tax on financial performance of SMEs in Nairobi
County and found that, performance of SMEs reduced due to high taxes.
On regression analysis, R-Squared was 0.505 which is a variation of 50.5% of financial
performance explained by tax incentives and which depicts a positive relationship of
financial performance and the independent variables. The analysis of variance showed
that tax incentives positively impacted on the performance of SACCOs
30
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
The findings, conclusion and recommendations of the analyzed data were discussed in
this chapter. These findings were pivoted on the purpose of research.
5.2 Summary of Findings
The objective of the research was to establish the effects of tax incentives on financial
performance of SACCOs in Nairobi County. The population study was all the 41 licensed
SACCOs. Descriptive statistics and data analysis were done on the secondary data
collected from SASRA using Stata. The normality test depicted that the variables were
not distributed normally, there was no heteroscedasticity and it showed absence of
multicollinearity on all the variables.
From correlation analysis, the findings revealed that capital allowance and accelerated
depreciation positively influenced ROA, while tax impacted negatively on ROA. R
squared was 0.505 which means 50.5% of variation of financial performance were
explained by tax incentives. From the analysis of variance, the probability significance
was 0.0025 which is below the significance level of 95% or 0.05, indicating the impact
on performance due to tax incentives.
The established regression equation for the study was;
Y= 0.1663761 + 4.190404X1- 0.0214859 X2 +1.190199X3
With the independent variables at a constant, from the above equation a unit increase in
capital allowance and in accelerated depreciation leads to an increase in financial
31
performance by 4.190404 units and 1.190199 units respectively. A rise in tax leads to a
decline in performance by 0.0214859 units.
5.3 Conclusion
From the analysis the research concluded that tax had a negative effect on financial
performance of SACCOs whereas both capital allowance and accelerated depreciation
had a positive significant effect. Therefore, SACCOs should strive to ensure that they get
capital allowance and accelerated depreciation incentives from the government to enable
them make more profits in subsequent years. The government therefore should offer
continuous tax incentives to SACCOs.
5.4 Recommendations
Currently the government does not levy tax on income interest which is the main source
of income for SACCOs, but tax is charged on all the other sources of income of
SACCOs, therefore, as far as the research findings and conclusion of this study are
concerned, the government should provide more tax incentives to the SACCOs especially
capital allowance and accelerated depreciation and tax exemptions, since an increase in
all of them increases the profitability of SACCOs. The government should also provide a
wide range of tax incentives which will encourage the establishment and continuous
growth of SACCOs. SACCOs should on the other hand be able to identify the various tax
incentives being offered by the government and work towards achieving them.
5.5 Limitations of the Study
This study used secondary data obtained from the audited financial reports of SACCOs
from SASRA from year 2014 to 2018 using a data collection sheet. The study faced
various limitations some of which include limited time to get the secondary data to be
32
used to accomplish the research. Firstly the researcher had to meet conditions such as
applying for a research permit, getting an authorization letter from the county
government in order to collect secondary data from SASRA. This was a challenge on the
side of the researcher as it required time to get those documents therefore, contributing to
collection of data within a short time. The other challenge is that while the researcher
thought that he will easily get the secondary data electronically, that was however not the
case, the researcher had to spend more time in manually collecting the data from financial
statement of various SACCOs, this proved tiresome and time consuming. Nairobi County
has more than 41 SACCOs, the researcher could not be able to get data for all the
SACCOs, because some of them were unlicensed by the government for failing to meet
certain given conditions. The secondary data is bound to be biased since it is submitted to
SASRA from different SACCOs who might not be accurate in reporting their financial
statements, this might compromise the research data for the study.
5.6 Suggestions for Further Research
The study sought to analyze the effects of tax incentives on financial performance of
SACCOs in Nairobi County. The researcher advocates for similar studies to be conducted
in other Counties apart from Nairobi County. The researcher also recommends studies to
be done on tax incentives and real estate firms in Kenya. The study further recommends
that research on tax incentives and performance of non-listed companies in NSE should
be carried out.
33
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38
APPENDICES
Appendix I: Data collection sheet
Name of
Sacco
Year/ Tax
Incentive
Accelerated
Depreciation
Incentives
Tax Rates
Incentives
Capital
Allowance
Incentives
Total Assets Net Profits
2014
2015
2016
2017
2018
Source: Research Findings
39
Appendix II: List of Registered SACCOs
No. NAME OF SAVINGS AND CREDIT COOPERATIVE SOCIETY
1 AFYA
2 AIRPORTS
3 ASILI
4 CHAI
5 CHUNA
6 COMOCO
7 ELIMU
8 FUNDILIMA
9 HARAMBEE
10 HAZINA
11 JAMII
12 KENPIPE
13 KENVERSITY
14 KENYA BANKERS
15 KENYA POLICE
16 KINGDOM
17 MAGEREZA
18 MAISHA BORA
19 MWALIMU NATIONAL
20 MWITO
21 NACICO
22 NAFAKA
23 NAKU
24 NASSEFU
25 NATION
26 NYATI
27 SAFARICOM
28 SHERIA
29 SHIRIKA
30 SHOPPERS
31 STIMA
32 TAQWA
33 TEMBO
34 UFANISI
35 UKRISTO NA UFANISI WA ANGALICANA
36 UKULIMA
37 UNAITAS
40
38 UNITED NATIONS
39 WANAANGA
40 WANANDEGE
41 WAUMINI
Source: Sasra
41
Appendix III: Research Data
SACCOS ROA Capital
Allowance
Tax Accelerated
Depreciation
1 2.087 0.030 0.850 0.577
2 0.109 0.008 0.058 0.008
3 0.121 0.024 0.772 0.010
4 0.057 0.013 3.303 0.025
5 0.080 0.006 0.305 0.017
6 0.026 0.009 0.889 0.012
7 0.038 0.018 1.491 0.015
8 0.027 0.010 0.008 0.739
9 0.016 0.073 2.270 0.009
10 2.197 0.052 0.163 0.016
11 0.112 0.018 0.551 0.029
12 0.154 0.006 0.418 0.087
13 0.092 0.002 0.624 0.046
14 0.018 0.003 3.714 0.024
15 0.148 0.022 0.199 0.011
16 0.032 0.004 0.507 0.027
17 0.169 0.081 2.096 0.019
18 3.031 0.025 1.170 0.070
19 0.950 0.031 0.562 0.122
20 0.287 0.006 0.417 0.171
21 0.210 0.015 0.606 0.019
22 0.101 0.019 0.202 0.010
23 0.017 0.022 1.038 0.065
24 0.826 0.064 0.181 0.101
25 0.314 0.087 0.460 0.023
26 1.082 0.099 0.405 0.008
27 0.163 0.013 0.669 0.025
28 0.215 0.055 0.340 0.005
29 0.103 0.041 0.309 0.007
30 0.079 0.072 0.278 0.004
31 0.133 0.070 0.311 0.021
32 0.089 0.016 0.570 0.006
33 0.097 0.053 0.791 0.002
34 0.132 0.004 0.773 0.020
35 0.141 0.021 0.690 0.021
36 0.170 0.013 0.239 0.008
37 0.153 0.008 2.615 0.006
38 0.026 0.096 0.361 0.082
39 0.141 0.078 - 0.043
40 0.296 0.001 0.380 0.074
41 0.219 0.042 0.294 0.057
Source: Sasra
